/output-plan-workflow

name: output-plan-workflow
description: Use when the user asks to create, build, generate, scaffold, or plan a new workflow. Orchestrates the full planning process including architecture, steps, prompts, evaluators, and testing strategy using specialized subagents.

Your task is to generate a comprehensive Output.ai workflow implementation plan in markdown format.

The plan will be displayed to the user who can then decide what to do with it.

Please respond with only the final version of the plan.

Use the todo tool to track your progress through the plan creation process.

Plan Creation Rules

Overview

Generate detailed specifications for implementation of a new workflow.

Output Path

All plan outputs go to: `.outputai/plans/YYYY_MM_DD_<workflow_name>_<task_name>/PLAN.md`

<process_flow>

<step number="0" name="arguments_analysis">

Step 0: Arguments Analysis

Analyze the arguments the user provided:

<substep number="0" name="arguments_analysis">

Ensure they have provided:

• workflow_description: The description of the workflow to be created
• additional_instructions: Additional instructions for the workflow

If not, ask the user for the missing information. </substep>

<substep number="1" name="pre_flight_check"> EXECUTE: Claude Skill: `output-meta-pre-flight` </substep>

</step>

<step number="1" name="context_gathering" subagent="workflow-context-fetcher">

Step 1: Context Gathering

Take the time to gather all the context you need to create a comprehensive plan.

1. Read any given files or links 2. Find any related workflows in the project 3. Read the projects documentation files

</step>

<step number="2" name="requirements_clarification">

Step 2: Requirements Clarification

Clarify scope boundaries and technical considerations by asking numbered questions as needed to ensure clear requirements before proceeding.

<clarification_areas> <scope>

• in_scope: what is included
• out_of_scope: what is excluded (optional)

</scope> <technical>

• functionality specifics
• UI/UX requirements
• integration points

</technical> <llm_provider>

• Ask which LLM provider the user wants to use (anthropic, openai, google-vertex, or amazon-bedrock)
• Default to anthropic if the user has no preference
• All prompt files in the workflow must use the same provider unless the user explicitly requests otherwise
• Record the chosen provider so it flows through to prompt engineering (step 6) and implementation
• If the provider's `@ai-sdk/*` package is not in `package.json` (new projects ship `@ai-sdk/anthropic` and `@ai-sdk/openai`), include installing it in the plan

</llm_provider> </clarification_areas>

<decision_tree> IF clarification_needed: ASK numbered_questions WAIT for_user_response ELSE: PROCEED schema_definition </decision_tree>

</step>

<step number="3" name="workflow_design" subagent="workflow-planner">

Step 3: Workflow Design

Design the workflow with clear single purpose steps and sound orchestration logic.

<thought_process>

1. Define the workflow name and description 2. What is a valid output schema for the workflow? 3. What is a valid input schema for the workflow? 4. What needs to happen to transform the input into the output? 5. What are the atomic steps that need to happen to transform the input into the output? 6. How do these steps relate to each other? 7. Are any of these steps conditional? 8. Are any of these steps already defined in the project? 9. How could these steps fail, and how should we handle them? (retry, backoff, etc.)

</thought_process>

<step_output> Output Draft Plan: to .outputai/plans/YYYY_MM_DD_<workflow_name>_<task_name>/PLAN.md </step_output>

</step>

<step number="4" name="step_design" subagent="workflow-planner">

Step 4: Step Design

Design the individual steps called by the workflow with clear boundaries.

<thought_process> 1. What is the name and description of each step? 2. What is the input schema for each step? 3. What is the output schema for each step? 4. What external services or APIs does each step use? 5. What error handling is needed for each step? 6. What retry policies should each step have? </thought_process>

<step_output> Output Updated Plan: to .outputai/plans/YYYY_MM_DD_<workflow_name>_<task_name>/PLAN.md </step_output>

</step>

<step number="4.5" name="evaluator_design" subagent="workflow-planner">

Step 4.5: Evaluator Design

Determine if the workflow requires quality assessment, validation, or content evaluation.

<decision_tree> IF workflow_outputs_need_quality_scoring: DESIGN evaluator functions IF workflow_has_llm_generated_content: CONSIDER content evaluation (factual accuracy, relevance, tone) IF workflow_requires_validation_with_confidence: DESIGN validation evaluators ELSE: SKIP evaluator design (note in plan: "No evaluators needed") </decision_tree>

<thought_process> 1. Does the workflow produce content that needs quality assessment? 2. Are there LLM-generated outputs that need evaluation? 3. Would the workflow benefit from confidence-scored validation? 4. What evaluation result types are appropriate (boolean/number/string)? 5. Should evaluators use simple logic or LLM-powered assessment? 6. Would offline eval testing with `@outputai/core/evals` be appropriate for dataset-driven verification? </thought_process>

<step_output> Output Updated Plan: to .outputai/plans/YYYY_MM_DD_<workflow_name>_<task_name>/PLAN.md </step_output>

</step>

<step number="5" name="plan_review" subagent="workflow-quality">

Step 5: Plan Review

Review the draft plan and make any necessary changes.

<thought_process> 1. Does the plan make sense? 2. Are all the steps clear and concise? 3. Are all the dependencies identified? 4. Does the workflow follow Output SDK conventions? 5. Are error handling patterns appropriate? 6. Is the input/output schema desi